VitPose+ Small
| Property | Value |
|---|---|
| License | Apache-2.0 |
| Paper | arXiv:2204.12484 |
| Authors | Yufei Xu, Jing Zhang, Qiming Zhang, Dacheng Tao |
| Training Data | MS COCO, AI Challenger, MPII, CrowdPose |
What is vitpose-plus-small?
VitPose+ Small is a lightweight implementation of the Vision Transformer (ViT) architecture specifically designed for human pose estimation. This model represents a significant advancement in the field by demonstrating that plain vision transformers, without complex architectural modifications, can achieve state-of-the-art performance in keypoint detection tasks.
Implementation Details
The model employs a non-hierarchical vision transformer backbone combined with a lightweight decoder for pose estimation. It's trained on multiple datasets including MS COCO, achieving 81.1 AP on the test-dev set. The architecture is designed to be scalable, ranging from 100M to 1B parameters, while maintaining high efficiency.
- Simple, non-hierarchical transformer architecture
- Flexible attention mechanisms and input resolution handling
- Knowledge transfer capabilities between model variants
- Trained on 8 A100 GPUs using the mmpose codebase
Core Capabilities
- Human keypoint detection across 17 body points
- Robust performance on occluded human instances
- Real-time pose estimation capabilities
- Adaptable to multiple pose estimation tasks
Frequently Asked Questions
Q: What makes this model unique?
VitPose+ Small stands out for its simplicity in design while achieving competitive performance. It demonstrates that complex architectural modifications aren't necessary for effective pose estimation, using a pure transformer-based approach that's both scalable and efficient.
Q: What are the recommended use cases?
The model is ideal for applications in human pose estimation, action recognition, surveillance systems, fitness tracking, and gaming applications. It's particularly effective in scenarios requiring accurate keypoint detection, even with partially occluded subjects.
